Geocomputation and Data Analysis with R
- Start date: TBC
- End date: TBC
- Duration: 1 Day
- Venue: Leeds Institute for Data Analytics, University of Leeds
The aim of Geocomputation and Data Analysis with R is to get you up-to-speed with high performance geographic processing, analysis, visualisation and modelling capabilities from the command-line. The course will be delivered in R, a statistical programming language popular in academia, industry and, increasingly, the public sector. It will teach a range of techniques using recent developments in the package sf and the ‘metapackage’ tidyverse, based on the open source book Geocomputation with R (Lovelace, Nowosad, and Meunchow 2019).
By the end of the course participants should:
- Be able to use R and RStudio as a powerful Geographic Information System (GIS)
- Know how R’s spatial capabilities fit within the landscape of open source GIS software
- Be confident with using R’s command-line interface (CLI) and scripting capabilities for geographic data processing
- Understand how to import a range of data sources into R
- Be able to perform a range of attribute operations such as subsetting and joining
- Understand how to implement a range of spatial data operations including spatial subsetting and spatial aggregation
- Have the confidence to output the results of geographic research in the form of static and interactive maps
Prior reading/ experience
If you are new to R, ensure you have completed a basic introductory course such as DataCamp’s introduction to R free course or equivalent.
If you’re interested in R for ‘data science’ and installing/updating/choosing R packages, these additional resources are recommended (these optional resources are all freely available online):
- The introductory chapter of R for Data Science
- Chapter 2 on setting-up R and section 4.4 on package selection in the book Efficient R Programming
Who should attend?
The course is open to students, academic staff and external delegates.
Robin Lovelace is a researcher at the Leeds Institute for Transport Studies (ITS) and the Leeds Institute for Data Analytics (LIDA). Robin has many years of experience of using R for academic research and has taught numerous R courses at all levels. He has developed popular R resources including the recently published book Efficient R Programming (Gillespie and Lovelace 2016), Introduction to Visualising Spatial Data in R and Spatial Microsimulation with R (Lovelace and Dumont 2016). These skills have been applied on a number of projects with real-world applications, including the Propensity to Cycle Tool, a nationally scalable interactive online mapping application, and the stplanr package.
This course has now finished. Should you have any enquiries, please email firstname.lastname@example.org.
Leeds Institute for Data Analytics
Level 11, Worsley Building
Institute for Transport Studies
Leeds LS2 9JT